In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import re
from imp import reload
import h5py
In [2]:
import deltascope as cranium
import deltascope.alignment as ut
In [3]:
at = ".\\Data\\30hpf\\AT\\Prob"
gfap = ".\\Data\\30hpf\\Gfap\\Prob"
In [4]:
outdir = ".\\Data\\30hpf\\Output-02-14-2019"
In [5]:
os.mkdir(outdir)
In [6]:
Dat = {}
for f in os.listdir(at):
if 'h5' in f:
num = re.findall(r'\d+',f.split('.')[0])[-1]
Dat[num] = os.path.join(at,f)
In [7]:
Dzrf = {}
for f in os.listdir(gfap):
if 'h5' in f:
num = re.findall(r'\d+',f.split('.')[0])[-1]
Dzrf[num] = os.path.join(gfap,f)
In [8]:
Dbat = {}
Dbzrf = {}
Data Preprocessing¶
In [9]:
klist = Dat.keys()
In [10]:
param = {
'gthresh':0.5,
'scale':[1,1,1],
'microns':[0.16,0.16,0.21],
'mthresh':0.5,
'radius':10,
'comp_order':[0,2,1],
'fit_dim':['x','z'],
'deg':2
}
In [11]:
%%time
for k in klist:
if k not in list(Dbat.keys()):
Dbat[k] = ut.preprocess(Dat[k],param)
Dbzrf[k] = ut.preprocess(Dzrf[k],param,pca=Dbat[k].pcamed,mm=Dbat[k].mm,vertex=Dbat[k].vertex)
print(k)
else:
print(k,'already processed')
In [12]:
def start(k):
return(ut.start(k,Dbat,[Dbzrf],im=True))
def save_both(k,dfa,dfb):
ut.save_both(k,dfa,dfb,outdir,'30hpf')
In [13]:
model = pd.DataFrame({'a':[],'b':[],'c':[]})
def save_model(k,mm,model):
row = pd.Series({'a':mm[0],'b':mm[1],'c':mm[2]},name=k)
model = model.append(row)
return(model)
def fit_model(axi,df,mm=None):
if mm == None:
mm = np.polyfit(df.x,df.z,2)
p = np.poly1d(mm)
xrange = np.arange(np.min(df.x),np.max(df.x))
axi.plot(xrange,p(xrange),c='m')
return(mm)
def pick_pts(x1,z1,vx,vz,x2,z2):
pts = pd.DataFrame({'x':[x1,vx,x2],'z':[z1,vz,z2]})
return(pts)
47¶
Discard because evidence of tear in zrf
In [14]:
k,df,Ldf,ax = start('47')
107¶
In [15]:
k,df,Ldf,ax = start('107')
In [16]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [17]:
pts.iloc[1].x = 35
pts.iloc[1].z = 12
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[17]:
In [18]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [19]:
df3,Ldf3,ax,p = ut.check_yz(df2,Ldf2)
In [20]:
p = np.poly1d([-3/4,0])
xrange = np.arange(-10,20)
ax[0,2].plot(xrange,p(xrange),c='c')
Out[20]:
In [21]:
df4,Ldf4,ax,p = ut.check_yz(df2,Ldf2,mm=[-3/4,0])
In [22]:
df5,Ldf5,mm,ax = ut.ch_vertex(df4,Ldf4)
In [23]:
save_both(k,df5,Ldf5[0])
In [24]:
model = save_model(k,mm,model)
102¶
In [25]:
k,df,Ldf,ax = start('102')
In [26]:
mm = fit_model(ax[0,1],df)
In [27]:
model = save_model(k,mm,model)
In [28]:
save_both(k,df,Ldf[0])
104¶
In [29]:
k,df,Ldf,ax = start('104')
In [30]:
mm = fit_model(ax[0,1],df)
In [31]:
model = save_model(k,mm,model)
In [32]:
save_both(k,df,Ldf[0])
5¶
Discard because abnormally low amounts of signal
In [33]:
k,df,Ldf,ax = start('5')
105¶
In [34]:
k,df,Ldf,ax = start('105')
In [35]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [36]:
pts.iloc[1].x = 28
pts.iloc[1].z = 21
pts.iloc[0].z = 24
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[36]:
In [37]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [38]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [39]:
save_both(k,df3,Ldf3[0])
In [40]:
model = save_model(k,mm,model)
44¶
In [41]:
k,df,Ldf,ax = start('44')
In [42]:
mm = fit_model(ax[0,1],df)
In [43]:
pts = pick_pts(-24,13,0,-3,26,14)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[43]:
In [44]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [45]:
save_both(k,df1,Ldf1[0])
In [46]:
model = save_model(k,mm,model)
110¶
In [47]:
k,df,Ldf,ax = start('110')
In [48]:
mm = fit_model(ax[0,1],df)
In [49]:
save_both(k,df,Ldf[0])
In [50]:
model = save_model(k,mm,model)
36¶
In [51]:
k,df,Ldf,ax = start('36')
In [52]:
mm = fit_model(ax[0,1],df)
In [53]:
save_both(k,df,Ldf[0])
In [54]:
model = save_model(k,mm,model)
103¶
In [55]:
k,df,Ldf,ax = start('103')
In [56]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [57]:
pts.iloc[1].x = -20
pts.iloc[1].z = 8
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[57]:
In [58]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [59]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [60]:
pts = pick_pts(-22,10,0,-2,23,10)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[60]:
In [61]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [62]:
save_both(k,df4,Ldf4[0])
In [63]:
model = save_model(k,mm,model)
2¶
In [64]:
k,df,Ldf,ax = start('2')
In [65]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [66]:
pts.iloc[1].x = -26
pts.iloc[1].z = 10
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[66]:
In [67]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [68]:
df2,Ldf2,mm,ax = ut.ch_vertex(df2,Ldf2)
In [69]:
save_both(k,df2,Ldf2[0])
In [70]:
model = save_model(k,mm,model)
106¶
In [71]:
k,df,Ldf,ax = start('106')
In [72]:
ax = ut.make_graph([Dbat[k].df_thresh,Dbzrf[k].df_thresh])
In [73]:
df = Dbat[k].df_thresh
Ldf = [Dbzrf[k].df_thresh]
In [74]:
df1,Ldf1,ax,p = ut.check_yz(Ldf[0],[df])
In [75]:
df2,Ldf2 = ut.flip(Ldf1[0],[df1])
ax = ut.make_graph([df2]+Ldf2)
In [76]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [77]:
df4,Ldf4,pts,ax = ut.check_pts(df3,Ldf3,'z')
In [78]:
pts.iloc[1].x = 55
pts.iloc[1].z = 15
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[78]:
In [79]:
df5,Ldf5,ax = ut.revise_pts(df3,Ldf3,'z',pts=pts)
In [80]:
pts = pick_pts(-2,17,25,0,55,15)
ax[1,1].scatter(pts.x,pts.z,c='m',s=50)
Out[80]:
In [81]:
df6,Ldf6,mm,ax = ut.ch_vertex(df5,Ldf5,pts=pts)
In [82]:
save_both(k,df6,Ldf6[0])
In [83]:
model = save_model(k,mm,model)
41¶
In [84]:
k,df,Ldf,ax = start('41')
In [85]:
mm = fit_model(ax[0,1],df)
In [86]:
save_both(k,df,Ldf[0])
In [87]:
model = save_model(k,mm,model)
1¶
In [88]:
k,df,Ldf,ax = start('1')
In [89]:
mm = fit_model(ax[0,1],df)
In [90]:
save_both(k,df,Ldf[0])
In [91]:
model = save_model(k,mm,model)
6¶
In [92]:
k,df,Ldf,ax = start('6')
In [93]:
mm = fit_model(ax[0,1],df)
In [94]:
save_both(k,df,Ldf[0])
In [95]:
model = save_model(k,mm,model)
7¶
In [96]:
k,df,Ldf,ax = start('7')
In [97]:
df1,Ldf1,ax,p = ut.check_yz(df,Ldf)
In [98]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [99]:
pts = pick_pts(-35,14,0,-3,30,12)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[99]:
In [100]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [101]:
save_both(k,df3,Ldf3[0])
In [102]:
model = save_model(k,mm,model)
4¶
In [103]:
k,df,Ldf,ax = start('4')
In [104]:
mm = fit_model(ax[0,1],df)
In [105]:
save_both(k,df,Ldf[0])
In [106]:
model = save_model(k,mm,model)
108¶
In [107]:
k,df,Ldf,ax = start('108')
In [108]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [109]:
df2,Ldf2,mm,ax = ut.ch_vertex(df1,Ldf1)
In [110]:
save_both(k,df2,Ldf2[0])
In [111]:
model = save_model(k,mm,model)
3¶
In [112]:
k,df,Ldf,ax = start('3')
In [113]:
mm = fit_model(ax[0,1],df)
In [114]:
save_both(k,df,Ldf[0])
In [115]:
model = save_model(k,mm,model)
Model¶
In [116]:
model.to_csv(os.path.join(outdir,'model.csv'))
In [117]:
outdir
Out[117]:
In [118]:
model
Out[118]: